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README.md

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+
+---
+
+license: apache-2.0
+language:
+  - en
+pipeline_tag: text-generation
+base_model: nvidia/Llama-3.1-Minitron-4B-Width-Base
+tags:
+  - chat
+
+---
+
+![](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)
+
+# QuantFactory/magnum-v2-4b-GGUF
+This is quantized version of [anthracite-org/magnum-v2-4b](https://huggingface.co/anthracite-org/magnum-v2-4b) created using llama.cpp
+
+# Original Model Card
+
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/658a46cbfb9c2bdfae75b3a6/9JwXZze4tHRGpc_RzE2AU.png)
+This is the eighth in a series of models designed to replicate the prose quality of the Claude 3 models, specifically Sonnet and Opus. This model is fine-tuned on top of [IntervitensInc/Llama-3.1-Minitron-4B-Width-Base-chatml](https://huggingface.co/IntervitensInc/Llama-3.1-Minitron-4B-Width-Base-chatml).
+
+## Prompting
+Model has been Instruct tuned with the ChatML formatting. A typical input would look like this:
+
+```py
+"""<|im_start|>system
+system prompt<|im_end|>
+<|im_start|>user
+Hi there!<|im_end|>
+<|im_start|>assistant
+Nice to meet you!<|im_end|>
+<|im_start|>user
+Can I ask a question?<|im_end|>
+<|im_start|>assistant
+"""
+```
+
+## Support
+
+To run inference on this model, you'll need to use Aphrodite, vLLM or EXL2/tabbyAPI, as llama.cpp hasn't yet merged the required pull request to fix the llama3.1 rope_freqs issue with custom head dimensions. 
+
+However, you can work around this by quantizing the model yourself to create a functional GGUF file. Note that until [this PR](https://github.com/ggerganov/llama.cpp/pull/9141) is merged, the context will be limited to 8k tokens.
+
+To create a working GGUF file, make the following adjustments:
+
+1. Remove the `"rope_scaling": {}` entry from `config.json`
+2. Change `"max_position_embeddings"` to `8192` in `config.json`
+
+These modifications should allow you to use the model with llama.cpp, albeit with the mentioned context limitation.
+
+## axolotl config
+
+<details><summary>See axolotl config</summary>
+
+axolotl version: `0.4.1`
+```yaml
+base_model: IntervitensInc/Llama-3.1-Minitron-4B-Width-Base-chatml
+model_type: AutoModelForCausalLM
+tokenizer_type: AutoTokenizer
+
+load_in_8bit: false
+load_in_4bit: false
+strict: false
+
+datasets:
+  - path: anthracite-org/Gryphe-3.5-16k-Subset
+    type: sharegpt
+    conversation: chatml
+  - path: Epiculous/Synthstruct-Gens-v1-Filtered-n-Cleaned
+    type: sharegpt
+    conversation: chatml
+  - path: anthracite-org/Stheno-Data-Filtered
+    type: sharegpt
+    conversation: chatml
+  - path: Epiculous/SynthRP-Gens-v1-Filtered-n-Cleaned
+    type: sharegpt
+    conversation: chatml
+  - path: lodrick-the-lafted/NopmWritingStruct
+    type: sharegpt
+    conversation: chatml
+  - path: anthracite-org/kalo-opus-instruct-22k-no-refusal
+    type: sharegpt
+    conversation: chatml
+
+chat_template: chatml
+
+val_set_size: 0.01
+output_dir: ./outputs/out
+
+adapter:
+lora_r:
+lora_alpha:
+lora_dropout:
+lora_target_linear:
+
+sequence_len: 16384
+# sequence_len: 32768
+sample_packing: true
+eval_sample_packing: false
+pad_to_sequence_len: true
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 32
+micro_batch_size: 1
+num_epochs: 2
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+learning_rate: 0.00002
+weight_decay: 0.05
+
+train_on_inputs: false
+group_by_length: false
+bf16: auto
+fp16:
+tf32: true
+
+gradient_checkpointing: true
+early_stopping_patience:
+resume_from_checkpoint:
+local_rank:
+logging_steps: 1
+xformers_attention:
+flash_attention: true
+
+warmup_ratio: 0.1
+evals_per_epoch: 4
+eval_table_size:
+eval_max_new_tokens: 128
+saves_per_epoch: 1
+
+debug:
+deepspeed:
+fsdp:
+fsdp_config:
+
+special_tokens:
+  pad_token: <|finetune_right_pad_id|>
+
+```
+
+</details><br>
+
+## Credits
+
+- [anthracite-org/Stheno-Data-Filtered](https://huggingface.co/datasets/anthracite-org/Stheno-Data-Filtered)
+- [anthracite-org/kalo-opus-instruct-22k-no-refusal](https://huggingface.co/datasets/anthracite-org/kalo-opus-instruct-22k-no-refusal)
+- [lodrick-the-lafted/NopmWritingStruct](https://huggingface.co/datasets/lodrick-the-lafted/NopmWritingStruct)
+- [NewEden/Gryphe-3.5-16k-Subset](https://huggingface.co/datasets/NewEden/Gryphe-3.5-16k-Subset) 
+- [Epiculous/Synthstruct-Gens-v1.1-Filtered-n-Cleaned](https://huggingface.co/datasets/Epiculous/Synthstruct-Gens-v1.1-Filtered-n-Cleaned)
+- [Epiculous/SynthRP-Gens-v1.1-Filtered-n-Cleaned](https://huggingface.co/datasets/Epiculous/SynthRP-Gens-v1.1-Filtered-n-Cleaned)
+
+This model has been a team effort, and the credits goes to all members of Anthracite.
+
+## Training
+The training was done for 2 epochs. We used  2 x [RTX 6000s](https://store.nvidia.com/en-us/nvidia-rtx/products/nvidia-rtx-6000-ada-generation/) GPUs graciously provided by [Kubernetes_Bad](https://huggingface.co/kubernetes-bad) for the full-parameter fine-tuning of the model.
+
+[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
+
+## Safety
+...